Multi-view approaches aim to resolve the scale and depth ambiguities that commonly occur in the single viewpoint setting and improve the accuracy of the estimated poses. Traditional works utilize local features [1]}, [2]} and cannot handle textureless objects. Recently, the multi-view object pose estimation problem has been revisited with neural networks. These approaches used an offline, batch-based optimization formulation, where all the frames are given at once, to obtain a single consistent scene interpretation [3]}, [4]}, [5]}, [6]}. Compared to batch-based methods, other works solve the multi-view pose estimation problem in an online manner. These works estimate camera poses and object poses simultaneously, known as object-level SLAM [7]}, [8]}, [9]}, [10]}, or estimate object poses with known camera poses [11]}, [12]}, [13]}. The frames are provided sequentially, and real-time performance is expected. Although these methods show performance improvements with only RGB images, they still face difficulty in dealing with object scales, rotational symmetries, and measurement uncertainties.
